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git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngine@58808 82a268e6-3cf1-43bd-a215-b396298e98cf

This commit is contained in:
Gianpaolo Coro 2012-09-20 13:26:36 +00:00
parent e6fe58bb2a
commit ab15cf8e6d
4 changed files with 162 additions and 4 deletions
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158
src/main/java/org/gcube/dataanalysis/ecoengine/test/checks/TestTransducers.java Normal file
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@ -0,0 +1,158 @@
package org.gcube.dataanalysis.ecoengine.test.checks;
import java.util.List;
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
import org.gcube.dataanalysis.ecoengine.evaluation.bioclimate.InterpolateTables.INTERPOLATIONFUNCTIONS;
import org.gcube.dataanalysis.ecoengine.interfaces.Transducerer;
import org.gcube.dataanalysis.ecoengine.processing.factories.TransducerersFactory;
import org.gcube.dataanalysis.ecoengine.test.regression.Regressor;
public class TestTransducers {
public static void main(String[] args) throws Exception {
System.out.println("TEST 1");
List<Transducerer> trans = null;
/*
trans = TransducerersFactory.getTransducerers(testConfigLocal());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal2());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal3());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal4());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal5());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal6());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
*/
trans = TransducerersFactory.getTransducerers(testConfigLocal7());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
}
private static AlgorithmConfiguration testConfigLocal() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("BIOCLIMATE_HSPEC");
config.setParam("HSPEC_TABLE_LIST", "hspec_validation"+AlgorithmConfiguration.getListSeparator()+"hspec_validation2");
config.setParam("HSPEC_TABLE_NAMES", "test"+AlgorithmConfiguration.getListSeparator()+"test");
config.setParam("Threshold", "0.5");
return config;
}
private static AlgorithmConfiguration testConfigLocal2() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("BIOCLIMATE_HCAF");
config.setParam("HCAF_TABLE_LIST","hcaf_d"+AlgorithmConfiguration.getListSeparator()+"hcaf_d_2016_linear_01332632269756"+AlgorithmConfiguration.getListSeparator()+"hcaf_d_2016_linear_01336062995861"+AlgorithmConfiguration.getListSeparator()+"hcaf_d_2050");
config.setParam("HCAF_TABLE_NAMES", "test"+AlgorithmConfiguration.getListSeparator()+"test"+AlgorithmConfiguration.getListSeparator()+"test"+AlgorithmConfiguration.getListSeparator()+"test");
return config;
}
private static AlgorithmConfiguration testConfigLocal3() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("BIOCLIMATE_HSPEN");
config.setParam("HSPEN_TABLE_LIST","hspen"+AlgorithmConfiguration.getListSeparator()+"hspen_2016"+AlgorithmConfiguration.getListSeparator()+"hspen_2020"+AlgorithmConfiguration.getListSeparator()+"hspen_2050");
config.setParam("HSPEN_TABLE_NAMES", "test"+AlgorithmConfiguration.getListSeparator()+"test"+AlgorithmConfiguration.getListSeparator()+"test"+AlgorithmConfiguration.getListSeparator()+"test");
return config;
}
private static AlgorithmConfiguration testConfigLocal4() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("HCAF_INTERPOLATION");
config.setParam("FirstHCAF","hcaf_d");
config.setParam("SecondHCAF","hcaf_d_2050");
config.setParam("YearStart","2012");
config.setParam("YearEnd","2050");
config.setParam("NumberOfInterpolations","2");
config.setParam("InterpolationFunction",INTERPOLATIONFUNCTIONS.LINEAR.name());
return config;
}
private static AlgorithmConfiguration testConfigLocal5() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("OCCURRENCES_MERGER");
config.setParam("longitudeColumn", "decimallongitude");
config.setParam("latitudeColumn", "decimallatitude");
config.setParam("recordedByColumn", "recordedby");
config.setParam("scientificNameColumn", "scientificname");
config.setParam("eventDateColumn", "eventdate");
config.setParam("lastModificationColumn", "modified");
config.setParam("rightTableName", "whitesharkoccurrences2");
config.setParam("leftTableName", "whitesharkoccurrences1");
config.setParam("finalTableName", "whitesharkoccurrencesmerged");
config.setParam("spatialTolerance", "0.5");
config.setParam("confidence", "80");
return config;
}
private static AlgorithmConfiguration testConfigLocal6() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("OCCURRENCES_INSEAS_ONEARTH");
config.setParam("longitudeColumn", "decimallongitude");
config.setParam("latitudeColumn", "decimallatitude");
config.setParam("OccurrencePointsTableName", "whitesharkoccurrences2");
config.setParam("finalTableName", "whitesharkoccurrencesfilteredseas");
config.setParam("FilterType", "IN_THE_WATER");
// config.setParam("FilterType", "ON_EARTH");
return config;
}
private static AlgorithmConfiguration testConfigLocal7() {
AlgorithmConfiguration config = Regressor.getConfig();
config.setAgent("OCCURRENCES_DUPLICATE_DELETER");
config.setParam("longitudeColumn", "decimallongitude");
config.setParam("latitudeColumn", "decimallatitude");
config.setParam("recordedByColumn", "recordedby");
config.setParam("scientificNameColumn", "scientificname");
config.setParam("eventDateColumn", "eventdate");
config.setParam("lastModificationColumn", "modified");
config.setParam("OccurrencePointsTableName", "occurrencetestduplicates");
config.setParam("finalTableName", "occurrencesnoduplicates");
config.setParam("spatialTolerance", "0.5");
config.setParam("confidence", "50");
return config;
}
}

4
src/main/java/org/gcube/dataanalysis/ecoengine/test/regression/RegressionTestTransducers.java
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@ -113,7 +113,7 @@ public static void main(String[] args) throws Exception {
config.setParam("leftTableName", "whitesharkoccurrences1"); config.setParam("leftTableName", "whitesharkoccurrences1");
config.setParam("finalTableName", "whitesharkoccurrencesmerged"); config.setParam("finalTableName", "whitesharkoccurrencesmerged");
config.setParam("spatialTolerance", "0.5"); config.setParam("spatialTolerance", "0.5");
config.setParam("confidence", "0.8"); config.setParam("confidence", "80");
return config; return config;
@ -148,7 +148,7 @@ public static void main(String[] args) throws Exception {
config.setParam("OccurrencePointsTableName", "whitesharkoccurrences2"); config.setParam("OccurrencePointsTableName", "whitesharkoccurrences2");
config.setParam("finalTableName", "whitesharkoccurrencesnoduplicates"); config.setParam("finalTableName", "whitesharkoccurrencesnoduplicates");
config.setParam("spatialTolerance", "0.5"); config.setParam("spatialTolerance", "0.5");
config.setParam("confidence", "0.8"); config.setParam("confidence", "80");
return config; return config;
} }

2
src/main/java/org/gcube/dataanalysis/ecoengine/transducers/OccurrencePointsDuplicatesDeleter.java
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@ -41,7 +41,7 @@ public class OccurrencePointsDuplicatesDeleter extends OccurrencePointsMerger{
ColumnType p8 = new ColumnType(leftTableNameF, lastModificationColumn, "column with Modified values", "modified", false); ColumnType p8 = new ColumnType(leftTableNameF, lastModificationColumn, "column with Modified values", "modified", false);
ServiceType p9 = new ServiceType(ServiceParameters.RANDOMSTRING, finalTableNameF, "Name of the resulting table", "processedOccurrences_"); ServiceType p9 = new ServiceType(ServiceParameters.RANDOMSTRING, finalTableNameF, "Name of the resulting table", "processedOccurrences_");
PrimitiveType p10 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, spatialTolerance, "The tolerance in degree for assessing that two points could be the same", "0.5"); PrimitiveType p10 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, spatialTolerance, "The tolerance in degree for assessing that two points could be the same", "0.5");
PrimitiveType p11 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, confidence, "The overall acceptance similarity threshold over which two points are the same", "0.8"); PrimitiveType p11 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, confidence, "The overall acceptance similarity threshold over which two points are the same - from 0 to 100", "80");
List<StatisticalType> inputs = new ArrayList<StatisticalType>(); List<StatisticalType> inputs = new ArrayList<StatisticalType>();
inputs.add(p1); inputs.add(p1);

2
src/main/java/org/gcube/dataanalysis/ecoengine/transducers/OccurrencePointsMerger.java
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@ -202,7 +202,7 @@ public class OccurrencePointsMerger implements Transducerer {
ColumnType p8 = new ColumnType(leftTableNameF, lastModificationColumn, "column with Modified values", "modified", false); ColumnType p8 = new ColumnType(leftTableNameF, lastModificationColumn, "column with Modified values", "modified", false);
ServiceType p9 = new ServiceType(ServiceParameters.RANDOMSTRING, finalTableNameF, "Name of the resulting table", "processedOccurrences_"); ServiceType p9 = new ServiceType(ServiceParameters.RANDOMSTRING, finalTableNameF, "Name of the resulting table", "processedOccurrences_");
PrimitiveType p10 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, spatialTolerance, "The tolerance in degree for assessing that two points could be the same", "0.5"); PrimitiveType p10 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, spatialTolerance, "The tolerance in degree for assessing that two points could be the same", "0.5");
PrimitiveType p11 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, confidence, "The overall acceptance similarity threshold over which two points are the same", "0.8"); PrimitiveType p11 = new PrimitiveType(Float.class.getName(), null, PrimitiveTypes.NUMBER, confidence, "The overall acceptance similarity threshold over which two points are the same - from 0 to 100", "80");
List<StatisticalType> inputs = new ArrayList<StatisticalType>(); List<StatisticalType> inputs = new ArrayList<StatisticalType>();
inputs.add(p1); inputs.add(p1);